The explosive growth in artificial intelligence has ushered in an era of technological marvels and groundbreaking achievements. Yet, amidst this revolution, a critical aspect often overlooked is the branding of AI models. The names assigned to these cutting-edge technologies are frequently a confusing jumble, leaving consumers and even industry professionals scratching their heads.
OpenAI, the company behind the widely recognized ChatGPT, dominates the field in terms of brand recognition. However, when it comes to selecting the right model for a specific task, users are confronted with a baffling array of options, such as “o3-mini-high” and “GPT-4o.” This week alone, the company unveiled three new models: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, further complicating the landscape.
It’s not just nascent startups that are guilty of branding their innovative technologies with a chaotic mix of names, version numbers, and parameter sizes. Even established tech giants like Google are contributing to the confusion. Google currently offers nine variations of its Gemini AI model, each with equally perplexing names like “Gemini 2.0 Flash Thinking Experimental,” “Gemini 1.0 Ultra,” and “Gemini 2.5 Pro Preview.”
To highlight the absurdity of AI model naming conventions, we’ve created a quiz that challenges you to distinguish between genuine and fabricated AI model names. We compiled a list of actual AI model names from a diverse range of AI companies and then crafted a list of fake names that mimic the patterns used by these companies.
The Naming Nightmare: A Quiz
Instructions: For each of the following AI model names, indicate whether you believe it to be a real or fake name. Answers are provided at the end.
- QuantumLeap AI
- Gemini 3.0 Supernova
- GPT-5 Turbo Max
- BrainWave X Pro
- AlphaMind 7.0
- DeepThought Prime
- NeuralNet Infinity
- Cognito AI Ultra
- Synapse 2.0 Plus
- LogicAI Xtreme
- Inferno Core
- Titan X Quantum
- Apex Vision Pro
- NovaMind AI
- Cortex 9.0 Ultimate
- Zenith AI Pro
- Polaris AI Genesis
- Vanguard AI Elite
- Horizon AI Max
- Galaxy AI Prime
Dissecting the Disorder: Why AI Model Names Are So Bad
The haphazard naming conventions employed by AI companies can be attributed to several factors:
Lack of Standardized Nomenclature: Unlike other scientific and technological fields, there is no established standard for naming AI models. This lack of uniformity allows companies to create names that are often inconsistent and confusing. The absence of a governing body or widely accepted guidelines means each company operates under its own set of rules, leading to a fragmented and often bewildering landscape for users. This problem is further compounded by the rapid pace of innovation in the AI field, which makes it difficult to establish and maintain consistent naming practices. Without a centralized effort to create and enforce standards, the confusion surrounding AI model names is likely to persist and even worsen as the technology continues to evolve. Consider the analogy to other fields like medicine or engineering, where precise terminology and consistent naming conventions are crucial for safety and effective communication. The AI industry would benefit greatly from adopting a similar approach.
Marketing Hype: AI companies often prioritize marketing appeal over clarity and precision when naming their models. They may opt for names that sound impressive or futuristic, even if they don’t accurately reflect the model’s capabilities. The allure of attracting investors and customers can often overshadow the importance of clear and informative naming. This can lead to the use of buzzwords and grandiose terms that ultimately obscure the true nature and functionality of the AI model. For example, a model might be named ‘QuantumLeap AI’ even if its performance gains are only incremental. While marketing is undoubtedly important, it should not come at the expense of clarity and transparency. A more balanced approach is needed, where marketing considerations are tempered by a commitment to providing accurate and understandable information about the AI model.
Technical Jargon: AI models are complex systems with numerous parameters and configurations. Companies may attempt to incorporate technical details into the names, resulting in cumbersome and impenetrable labels. The desire to convey the technical sophistication of an AI model can sometimes lead to the inclusion of overly technical terms in its name. This can be particularly problematic for non-technical users who may find these names intimidating and difficult to understand. For instance, a model might be named ‘Transformer-XL-15B-v2’ which, while informative to AI researchers, is largely meaningless to the average user. A more user-friendly approach would be to reserve the technical details for the model’s documentation and use a simpler, more descriptive name for general communication.
Rapid Innovation: The field of AI is evolving at an unprecedented pace, with new models and versions being released frequently. This rapid innovation can lead to a proliferation of names, further exacerbating the confusion. The constant stream of new AI models and versions creates a challenge for users trying to keep up with the latest developments. Each new model often comes with its own unique name, adding to the already overwhelming number of options. This rapid churn can make it difficult for users to make informed decisions about which model is best suited for their needs. A more structured approach to versioning and naming, perhaps with a focus on highlighting significant improvements and features, could help to alleviate this confusion.
Internal Naming Conventions: Some AI companies may use internal naming conventions that are not intended for public consumption. However, these internal names may inadvertently leak into marketing materials or product documentation, adding to the overall confusion. Internal naming conventions, while useful for organizing and managing AI models within a company, can often be cryptic and meaningless to external users. When these internal names find their way into public-facing materials, they can further contribute to the confusion surrounding AI model naming. A clear separation between internal and external naming conventions is essential, with a focus on using user-friendly names for all public communications.
The Consequences of Confusing Names
The confusing naming conventions used for AI models have several negative consequences:
Customer Confusion: Customers may struggle to understand the differences between various AI models, making it difficult to choose the right model for their needs. This directly impacts the usability and adoption of AI technologies. When potential users are faced with a plethora of models with similar-sounding names, they may become overwhelmed and abandon their search for the appropriate solution. This confusion can lead to missed opportunities and slower adoption rates for AI across various industries.
Reduced Adoption: The complexity of AI model names can deter potential users from adopting the technology, as they may feel overwhelmed or intimidated. The perceived complexity associated with AI, often exacerbated by confusing names, can act as a barrier to entry for many users. This is particularly true for individuals and small businesses who may lack the technical expertise to navigate the complex landscape of AI models. Simplifying the naming conventions and providing clear explanations of each model’s capabilities can help to make AI more accessible to a wider audience.
Brand Dilution: Inconsistent and confusing names can dilute the brand image of AI companies, making it difficult for them to establish a clear identity in the market. A strong brand identity is crucial for AI companies to stand out in a crowded market. However, if a company’s AI models are named in a haphazard and inconsistent manner, it can weaken the overall brand image and make it difficult for customers to associate the company with specific capabilities or values. A more strategic approach to naming can help to reinforce the brand and create a stronger connection with customers.
Communication Challenges: The lack of standardized nomenclature can hinder communication between AI professionals, making it difficult to discuss and compare different models. The absence of a common language for describing AI models can create communication barriers among researchers, developers, and other professionals in the field. This can impede collaboration and slow down the pace of innovation. Adopting a standardized nomenclature would facilitate more effective communication and enable a more efficient exchange of knowledge.
Increased Training Costs: Companies may need to invest more resources in training employees to understand the various AI models and their corresponding names. The need to train employees on the nuances of various AI models and their often-confusing names can add significant costs to companies adopting AI technologies. This is particularly true for larger organizations with a diverse workforce. Simplifying the naming conventions and providing clear documentation can help to reduce training costs and improve employee productivity.
A Call for Clarity: Towards Better AI Model Naming
To address the problem of confusing AI model names, the industry needs to adopt a more standardized and user-friendly approach. Here are some recommendations:
Establish a Naming Convention: Develop a clear and consistent naming convention that incorporates key information about the AI model, such as its architecture, training data, and performance metrics. A well-defined naming convention should provide a framework for consistently naming AI models across different companies and organizations. This convention should include guidelines for incorporating key information such as the model’s architecture (e.g., Transformer, CNN), the type of training data used (e.g., text, images, audio), and relevant performance metrics (e.g., accuracy, speed).
Prioritize Clarity: Choose names that are easy to understand and remember, avoiding technical jargon and marketing hype. Simplicity and clarity should be paramount when naming AI models. Avoid using overly technical terms or marketing buzzwords that can obscure the true nature of the model. Instead, focus on choosing names that are easy to understand and remember for both technical and non-technical users.
Focus on Functionality: Emphasize the specific capabilities and applications of the AI model in the name, rather than focusing on abstract concepts. The name of an AI model should ideally convey its intended purpose and the specific tasks it is designed to perform. For example, a model designed for image recognition could be named ‘ImageRecognitionAI’ rather than a more abstract name like ‘VisionaryAI’. This helps users quickly understand the model’s capabilities and determine if it is suitable for their needs.
Use Version Numbers Consistently: Adopt a consistent version numbering system to track updates and improvements to the AI model. A consistent version numbering system is crucial for tracking changes and improvements to AI models over time. This allows users to easily identify the latest version of a model and understand the differences between different versions. A common approach is to use semantic versioning (e.g., 1.0.0, 1.1.0, 2.0.0) which provides a clear indication of the type of changes that have been made.
Provide Clear Documentation: Offer comprehensive documentation that explains the various AI models and their corresponding names in detail. Comprehensive documentation is essential for helping users understand the capabilities and limitations of different AI models. This documentation should include a clear explanation of the model’s name, its architecture,training data, performance metrics, and intended use cases.
Engage with the Community: Solicit feedback from users and experts to refine the naming convention and improve the overall user experience. Gathering feedback from users and experts is crucial for refining the naming convention and ensuring that it meets the needs of the community. This can be done through surveys, focus groups, and online forums. By actively engaging with the community, AI companies can create a naming convention that is both effective and user-friendly. The AI community’s active participation can also ensure that the naming conventions stay relevant as technology continues to evolve. Furthermore, this engagement fosters a sense of shared ownership and encourages widespread adoption of the agreed-upon standards.
The Future of AI Model Naming
As AI technology continues to evolve, the importance of clear and consistent naming conventions will only increase. By adopting a more user-friendly approach to naming, the industry can reduce confusion, promote adoption, and foster better communication.
The challenge lies in striking a balance between technical accuracy, marketing appeal, and user comprehension. AI companies need to move beyond the current practice of haphazard naming and embrace a more strategic and thoughtful approach. The future of AI depends not only on the advancements in technology but also on the ability to communicate those advancements effectively to the world. In addition to establishing clear and consistent naming conventions, ongoing education and awareness initiatives are crucial. These efforts can help users develop a better understanding of AI models and their capabilities, enabling them to make more informed decisions. Collaborations between AI companies, academic institutions, and industry organizations can play a vital role in promoting these initiatives and fostering a culture of clarity and transparency in the AI field. Ultimately, the success of AI depends on its ability to be understood and trusted by a wide range of users, and clear and consistent naming conventions are a critical step in achieving this goal.
Answers to the Quiz
Here are the answers to the AI model name quiz:
- QuantumLeap AI: Fake
- Gemini 3.0 Supernova: Fake
- GPT-5 Turbo Max: Fake
- BrainWave X Pro: Fake
- AlphaMind 7.0: Fake
- DeepThought Prime: Fake
- NeuralNet Infinity: Fake
- Cognito AI Ultra: Fake
- Synapse 2.0 Plus: Fake
- LogicAI Xtreme: Fake
- Inferno Core: Fake
- Titan X Quantum: Fake
- Apex Vision Pro: Fake
- NovaMind AI: Fake
- Cortex 9.0 Ultimate: Fake
- Zenith AI Pro: Fake
- Polaris AI Genesis: Fake
- Vanguard AI Elite: Fake
- Horizon AI Max: Fake
- Galaxy AI Prime: Fake
Note: All names in this quiz were fabricated to illustrate the common patterns and styles used in AI model naming.